PROJECT 4 ABSTRACT High dimensional human microbiome and DNA methylation data offer great promise to contributing to the understanding of the underlying etiology of a myriad of human diseases. Mediation modeling is a critical tool used in molecular epidemiology to infer causal pathways for biological processes. As yet, mediation modeling has not been extensively applied to studies of the microbiome and epigenome even though it is likely to clarify their critical roles in disease pathogenesis. To our knowledge, there are no available mediation models to test whether the human microbiome mediates disease occurrence. Impediments to using mediation modeling arise from the compositional, phylogenetically hierarchical, sparse, and high dimensional structure of microbiome data. Another level of complexity is that mediations can occur through changes in individual microbes or through alterations to the overall community structure of the microbiome. For DNA methylation data, models exist for analyzing mediational effects; however, current methods rely on reference data to adjust for cell- composition effects. Yet reference data are often not available and are costly to obtain. Reference-free approaches have been proposed for association analyses to resolve this issue, but these have not been applied to mediation analyses. To address these critical challenges, we will develop new mediation methods to analyze high-dimensional data on the human microbiome and DNA methylation as complex mediators in disease causing pathways. We will apply our models to test the effects of the infant gut microbiome, breast milk microbiome, cord blood DNA methylome, and breast milk DNA methylome in mediating the associations between prenatal exposures (e.g. arsenic exposure) and childhood infections and allergy/atopy in the first year of life using the rich data from the large ongoing longitudinal molecular epidemiologic New Hampshire Birth Cohort Study. R packages will be developed to implement these two models. These methods will enable the identification of complex mediators of disease pathways to highlight opportunities for designing interventions to support children's health and development.